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      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
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    "740a86e7b97d473b84f614555a425528": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "ProgressStyleModel",
     "model_module_version": "1.5.0",
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      "_model_name": "ProgressStyleModel",
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  }
 },
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "# 确保文件存在,搭载云盘\n",
    "import os\n",
    "my_files = '/content/drive/MyDrive/fine_tune_log'\n",
    "for dirname, _, filenames in os.walk(my_files):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_NL0M-W4rK52",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1711959138756,
     "user_tz": -480,
     "elapsed": 556,
     "user": {
      "displayName": "Lawrence Kraft",
      "userId": "13357759838929016886"
     }
    },
    "outputId": "6856de71-d7c4-4228-89be-a298ab5ab779"
   },
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/content/drive/MyDrive/fine_tune_log/Mistral_7B_fine_tuning_Guide.ipynb\n",
      "/content/drive/MyDrive/fine_tune_log/LLM_Fine_Tuning.ipynb\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "KvSMt_5GqG2L",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1711959012011,
     "user_tz": -480,
     "elapsed": 454370,
     "user": {
      "displayName": "Lawrence Kraft",
      "userId": "13357759838929016886"
     }
    },
    "outputId": "a38434ae-c2a0-4379-d778-ec9951627b39"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m102.2/102.2 MB\u001B[0m \u001B[31m8.7 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m23.7/23.7 MB\u001B[0m \u001B[31m40.6 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m823.6/823.6 kB\u001B[0m \u001B[31m61.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
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      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m21.1/21.1 MB\u001B[0m \u001B[31m6.9 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[?25h  Installing build dependencies ... \u001B[?25l\u001B[?25hdone\n",
      "  Getting requirements to build wheel ... \u001B[?25l\u001B[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001B[?25l\u001B[?25hdone\n",
      "  Building wheel for transformers (pyproject.toml) ... \u001B[?25l\u001B[?25hdone\n",
      "  Installing build dependencies ... \u001B[?25l\u001B[?25hdone\n",
      "  Getting requirements to build wheel ... \u001B[?25l\u001B[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001B[?25l\u001B[?25hdone\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m290.1/290.1 kB\u001B[0m \u001B[31m2.4 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[?25h  Building wheel for peft (pyproject.toml) ... \u001B[?25l\u001B[?25hdone\n",
      "  Installing build dependencies ... \u001B[?25l\u001B[?25hdone\n",
      "  Getting requirements to build wheel ... \u001B[?25l\u001B[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001B[?25l\u001B[?25hdone\n",
      "  Building wheel for accelerate (pyproject.toml) ... \u001B[?25l\u001B[?25hdone\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m225.0/225.0 kB\u001B[0m \u001B[31m2.3 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
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      "\u001B[?25h  Preparing metadata (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m312.4/312.4 kB\u001B[0m \u001B[31m7.7 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
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      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m58.3/58.3 kB\u001B[0m \u001B[31m5.4 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m71.5/71.5 kB\u001B[0m \u001B[31m5.5 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[?25h  Building wheel for ffmpy (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "torchaudio 2.2.1+cu121 requires torch==2.2.1, but you have torch 2.2.2 which is incompatible.\n",
      "torchtext 0.17.1 requires torch==2.2.1, but you have torch 2.2.2 which is incompatible.\n",
      "torchvision 0.17.1+cu121 requires torch==2.2.1, but you have torch 2.2.2 which is incompatible.\u001B[0m\u001B[31m\n",
      "\u001B[0m"
     ]
    }
   ],
   "source": [
    "# 基础环境\n",
    "!pip install -q -U bitsandbytes\n",
    "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
    "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
    "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
    "!pip install -q trl xformers wandb datasets einops gradio sentencepiece"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# 引入需要的Gath_baize数据集,Mistral-7B模型\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,HfArgumentParser,TrainingArguments,pipeline, logging, TextStreamer\n",
    "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model\n",
    "import os,torch, wandb, platform, gradio, warnings\n",
    "from datasets import load_dataset\n",
    "from trl import SFTTrainer\n",
    "from huggingface_hub import notebook_login\n",
    "base_model, dataset_name, new_model = \"mistralai/Mistral-7B-v0.1\" , \"gathnex/Gath_baize\", \"gathnex/Gath_mistral_7b\""
   ],
   "metadata": {
    "id": "Cih8LizWr7M6",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1711959120825,
     "user_tz": -480,
     "elapsed": 19063,
     "user": {
      "displayName": "Lawrence Kraft",
      "userId": "13357759838929016886"
     }
    }
   },
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# data\n",
    "dataset = load_dataset(dataset_name, split=\"train\")\n",
    "dataset[\"chat_sample\"][0]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150,
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      "cee2fa790dcb4d3f97765e9ffb2440f5"
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    },
    "id": "KUY0GxtIt2hn",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1711959185620,
     "user_tz": -480,
     "elapsed": 21167,
     "user": {
      "displayName": "Lawrence Kraft",
      "userId": "13357759838929016886"
     }
    },
    "outputId": "a5058024-17fc-4180-cf2d-0a9dea203418"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/21.0 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
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      }
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     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading data:   0%|          | 0.00/222M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "4ab1aefde4b14232a2ba4a0aeff57369"
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     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f182bed64f0042ea87c035f7cb5fddde"
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     "metadata": {}
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    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'The conversation between Human and AI assisatance named Gathnex [INST] Generate a headline given a content block.\\nThe Sony Playstation 5 is the latest version of the console. It has improved graphics and faster processing power.\\n[/INST] Experience Amazing Graphics and Speed with the New Sony Playstation 5'"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
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     },
     "metadata": {},
     "execution_count": 6
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# Load base model(Mistral 7B)\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit= True,\n",
    "    bnb_4bit_quant_type= \"nf4\",\n",
    "    bnb_4bit_compute_dtype= torch.bfloat16,\n",
    "    bnb_4bit_use_double_quant= False,\n",
    ")\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "   base_model,\n",
    "    quantization_config=bnb_config,\n",
    "    device_map={\"\": 0}\n",
    ")\n",
    "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
    "model.config.pretraining_tp = 1\n",
    "model.gradient_checkpointing_enable()\n",
    "# Load tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
    "tokenizer.padding_side = 'right'\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.add_eos_token = True\n",
    "tokenizer.add_bos_token, tokenizer.add_eos_token"
   ],
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   "execution_count": 7,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
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     "data": {
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     "data": {
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "#Adding the adapters in the layers\n",
    "model = prepare_model_for_kbit_training(model)\n",
    "peft_config = LoraConfig(\n",
    "        r=16,\n",
    "        lora_alpha=16,\n",
    "        lora_dropout=0.05,\n",
    "        bias=\"none\",\n",
    "        task_type=\"CAUSAL_LM\",\n",
    "        target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\"gate_proj\"]\n",
    "    )\n",
    "model = get_peft_model(model, peft_config)"
   ],
   "metadata": {
    "id": "VLGWRhw_wRX0",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1711960066969,
     "user_tz": -480,
     "elapsed": 752,
     "user": {
      "displayName": "Lawrence Kraft",
      "userId": "13357759838929016886"
     }
    }
   },
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from google.colab import userdata\n",
    "WANDB_API_KEY = userdata.get('WANDB_API_KEY')\n",
    "# Monitering the LLM\n",
    "wandb.login(key =WANDB_API_KEY)\n",
    "run = wandb.init(project='Fine tuning mistral 7B', job_type=\"training\", anonymous=\"allow\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 160
    },
    "id": "4C1Ql1njvei_",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1711960732340,
     "user_tz": -480,
     "elapsed": 35697,
     "user": {
      "displayName": "Lawrence Kraft",
      "userId": "13357759838929016886"
     }
    },
    "outputId": "aea7bcb6-8975-4296-f57f-c02e8e0f9c6f"
   },
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
      "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[33mWARNING\u001B[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
      "\u001B[34m\u001B[1mwandb\u001B[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "Tracking run with wandb version 0.16.5"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "Run data is saved locally in <code>/content/wandb/run-20240401_083818-xsg597bi</code>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/sisconsavior/Fine%20tuning%20mistral%207B/runs/xsg597bi/workspace' target=\"_blank\">efficient-leaf-2</a></strong> to <a href='https://wandb.ai/sisconsavior/Fine%20tuning%20mistral%207B' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       " View project at <a href='https://wandb.ai/sisconsavior/Fine%20tuning%20mistral%207B' target=\"_blank\">https://wandb.ai/sisconsavior/Fine%20tuning%20mistral%207B</a>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       " View run at <a href='https://wandb.ai/sisconsavior/Fine%20tuning%20mistral%207B/runs/xsg597bi/workspace' target=\"_blank\">https://wandb.ai/sisconsavior/Fine%20tuning%20mistral%207B/runs/xsg597bi/workspace</a>"
      ]
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "#Hyperparamter\n",
    "training_arguments = TrainingArguments(\n",
    "    output_dir= \"./results\",\n",
    "    num_train_epochs= 2,\n",
    "    per_device_train_batch_size= 8,\n",
    "    gradient_accumulation_steps= 2,\n",
    "    optim = \"paged_adamw_8bit\",\n",
    "    save_steps= 1000,\n",
    "    logging_steps= 30,\n",
    "    learning_rate= 2e-4,\n",
    "    weight_decay= 0.001,\n",
    "    fp16= False,\n",
    "    bf16= False,\n",
    "    max_grad_norm= 0.3,\n",
    "    max_steps= -1,\n",
    "    warmup_ratio= 0.3,\n",
    "    group_by_length= True,\n",
    "    lr_scheduler_type= \"constant\",\n",
    "    report_to=\"wandb\"\n",
    ")\n",
    "# Setting sft parameters\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=dataset,\n",
    "    peft_config=peft_config,\n",
    "    max_seq_length= None,\n",
    "    dataset_text_field=\"chat_sample\",\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_arguments,\n",
    "    packing= False,\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86,
     "referenced_widgets": [
      "13d81209cd914b67a8c407582a3e9438",
      "bee48674cba748cea803ddf69261c625",
      "ad809e568b894762820ac97bf4672c1d",
      "2da0388655934f549ba24e4e07df3a25",
      "8706a4835d8a427689f4d253ebe83901",
      "beed3ec4dc0f4a1a9f1d5975449a81c1",
      "9e8ac70ba44d402a8a235cd6ef833ce5",
      "4564c88e3284413f8bd4bdd578c6b414",
      "1753c99034be48c88e36db6406f99b2f",
      "ade284adc95141d6b71c844205fdc747",
      "c8b8081355d84d8a8a5702d5e3584b2b"
     ]
    },
    "id": "y416SjbVzZ79",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1711960936267,
     "user_tz": -480,
     "elapsed": 138669,
     "user": {
      "displayName": "Lawrence Kraft",
      "userId": "13357759838929016886"
     }
    },
    "outputId": "3c5741c7-f327-4b59-9553-434072b787e9"
   },
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:245: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/210311 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "13d81209cd914b67a8c407582a3e9438"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# Save the fine-tuned model\n",
    "trainer.model.save_pretrained(new_model)\n",
    "wandb.finish()\n",
    "model.config.use_cache = True\n",
    "model.eval()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "f682bc2d32074556a8843ed66d69fc48",
      "a797a89a6769448aa78a74236949ad8c",
      "28a397549e714091a3d628c949ffd068",
      "5a238dd0e1674872a33fb2f355e0d5cb",
      "0db7eb9b263f433fa5b8308b47665469",
      "55358e69b8514c628dbd8552236bd3e5",
      "84b7ee356c86425dbaadd50ee5e705ac",
      "740a86e7b97d473b84f614555a425528"
     ]
    },
    "id": "3-5Tj7hC1D3S",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1711962058737,
     "user_tz": -480,
     "elapsed": 1014225,
     "user": {
      "displayName": "Lawrence Kraft",
      "userId": "13357759838929016886"
     }
    },
    "outputId": "55a070c3-24f7-4e46-ca9e-615221b45df6"
   },
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "VBox(children=(Label(value='0.011 MB of 0.011 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f682bc2d32074556a8843ed66d69fc48"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\u001B[34m\u001B[1mwandb\u001B[0m: \u001B[32m\u001B[41mERROR\u001B[0m Control-C detected -- Run data was not synced\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "PeftModelForCausalLM(\n",
       "  (base_model): LoraModel(\n",
       "    (model): MistralForCausalLM(\n",
       "      (model): MistralModel(\n",
       "        (embed_tokens): Embedding(32000, 4096)\n",
       "        (layers): ModuleList(\n",
       "          (0-31): 32 x MistralDecoderLayer(\n",
       "            (self_attn): MistralSdpaAttention(\n",
       "              (q_proj): lora.Linear4bit(\n",
       "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
       "                (lora_dropout): ModuleDict(\n",
       "                  (default): Dropout(p=0.05, inplace=False)\n",
       "                )\n",
       "                (lora_A): ModuleDict(\n",
       "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
       "                )\n",
       "                (lora_B): ModuleDict(\n",
       "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
       "                )\n",
       "                (lora_embedding_A): ParameterDict()\n",
       "                (lora_embedding_B): ParameterDict()\n",
       "              )\n",
       "              (k_proj): lora.Linear4bit(\n",
       "                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
       "                (lora_dropout): ModuleDict(\n",
       "                  (default): Dropout(p=0.05, inplace=False)\n",
       "                )\n",
       "                (lora_A): ModuleDict(\n",
       "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
       "                )\n",
       "                (lora_B): ModuleDict(\n",
       "                  (default): Linear(in_features=16, out_features=1024, bias=False)\n",
       "                )\n",
       "                (lora_embedding_A): ParameterDict()\n",
       "                (lora_embedding_B): ParameterDict()\n",
       "              )\n",
       "              (v_proj): lora.Linear4bit(\n",
       "                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
       "                (lora_dropout): ModuleDict(\n",
       "                  (default): Dropout(p=0.05, inplace=False)\n",
       "                )\n",
       "                (lora_A): ModuleDict(\n",
       "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
       "                )\n",
       "                (lora_B): ModuleDict(\n",
       "                  (default): Linear(in_features=16, out_features=1024, bias=False)\n",
       "                )\n",
       "                (lora_embedding_A): ParameterDict()\n",
       "                (lora_embedding_B): ParameterDict()\n",
       "              )\n",
       "              (o_proj): lora.Linear4bit(\n",
       "                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
       "                (lora_dropout): ModuleDict(\n",
       "                  (default): Dropout(p=0.05, inplace=False)\n",
       "                )\n",
       "                (lora_A): ModuleDict(\n",
       "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
       "                )\n",
       "                (lora_B): ModuleDict(\n",
       "                  (default): Linear(in_features=16, out_features=4096, bias=False)\n",
       "                )\n",
       "                (lora_embedding_A): ParameterDict()\n",
       "                (lora_embedding_B): ParameterDict()\n",
       "              )\n",
       "              (rotary_emb): MistralRotaryEmbedding()\n",
       "            )\n",
       "            (mlp): MistralMLP(\n",
       "              (gate_proj): lora.Linear4bit(\n",
       "                (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
       "                (lora_dropout): ModuleDict(\n",
       "                  (default): Dropout(p=0.05, inplace=False)\n",
       "                )\n",
       "                (lora_A): ModuleDict(\n",
       "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
       "                )\n",
       "                (lora_B): ModuleDict(\n",
       "                  (default): Linear(in_features=16, out_features=14336, bias=False)\n",
       "                )\n",
       "                (lora_embedding_A): ParameterDict()\n",
       "                (lora_embedding_B): ParameterDict()\n",
       "              )\n",
       "              (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
       "              (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False)\n",
       "              (act_fn): SiLU()\n",
       "            )\n",
       "            (input_layernorm): MistralRMSNorm()\n",
       "            (post_attention_layernorm): MistralRMSNorm()\n",
       "          )\n",
       "        )\n",
       "        (norm): MistralRMSNorm()\n",
       "      )\n",
       "      (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def stream(user_prompt):\n",
    "    runtimeFlag = \"cuda:0\"\n",
    "    system_prompt = 'The conversation between Human and AI assisatance named Gathnex\\n'\n",
    "    B_INST, E_INST = \"[INST]\", \"[/INST]\"\n",
    "\n",
    "    prompt = f\"{system_prompt}{B_INST}{user_prompt.strip()}\\n{E_INST}\"\n",
    "\n",
    "    inputs = tokenizer([prompt], return_tensors=\"pt\").to(runtimeFlag)\n",
    "\n",
    "    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
    "\n",
    "    _ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "stream(\"what is newtons 2rd law and its formula\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Clear the memory footprint\n",
    "del model, trainer\n",
    "torch.cuda.empty_cache()\n",
    "\n",
    "# Reload the base model\n",
    "base_model_reload = AutoModelForCausalLM.from_pretrained(\n",
    "    base_model, low_cpu_mem_usage=True,\n",
    "    return_dict=True,torch_dtype=torch.bfloat16,\n",
    "    device_map= {\"\": 0})\n",
    "model = PeftModel.from_pretrained(base_model_reload, new_model)\n",
    "model = model.merge_and_unload()\n",
    "\n",
    "# Reload tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#push the model to hub\n",
    "!huggingface-cli login\n",
    "model.push_to_hub(new_model, use_temp_dir=False)\n",
    "tokenizer.push_to_hub(new_model, use_temp_dir=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ]
}
